【摘要】 方面情感三元组抽取旨在识别一条评论中的方面项及其情感倾向，并提取与其相关的观点项.现有方法大多将该类任务分为多个子任务，将子任务组成流水线并完成这类任务.然而，基于流水线思想的方法在实际应用中会受到误差传播、不易使用等因素的影响.为此，文中提出词对关系学习方法，将方面情感三元组抽取任务转化为端到端的词对关系学习任务.方法包含一种可将句中的词对关系进行统一标注以表示所有三元组的词对关系标注的方法，以及为此特别构建的可输出词对关系的词对关系网络.首先，使用双向门控循环单元和混合式注意力对句子进行编码表示.然后，使用注意力图转换模块将句子编码转换为各项标签概率.最后，从词对关系标签结果中提取三元组.此外，将预训练的BERT(Bidirectional Encoder Representation from Transformer)应用于文中方法.在4个标准数据集上的实验表明，文中方法性能较优.更多还原

【Abstract】 Aspect sentiment triplet extraction is designed to identify aspect items with their sentiment tendencies in a comment and to extract the related opinion items. In most of the existing methods, this type of task is divided into several sub-tasks, and then the task is completed by the pipeline composed of the sub-tasks. However, the methods based on pipeline are affected by error propagation and inconvenience for use in practice. Therefore, a word-pair relation learning method for aspect sentiment triplet extraction is proposed, which transforms the aspect sentiment triplet extraction task into an end-to-end word-pair relation learning task. The method contains a word-pair relation tagging scheme, which can unify word-pair relations in sentences to represent all triplets, and a specially built word-pair relation network to output word-pair relation. Firstly, the sentence is encoded by bidirectional grated recurrent unit and mixed attention. Then, sentence coding is converted into tag probabilities through the attention map transform module. Finally, the triplets are extracted from the result of the word-pair relation tag. In addition, the pre-trained bidirectional encoder representation from transformer is applied to the proposed method. Experiments on four standard datasets show that the proposed method is superior.更多还原